Assessment of Salt Marsh Change on Assateague Island National Seashore Between 1962 and 2016

2020 ◽  
Vol 86 (3) ◽  
pp. 187-194 ◽  
Author(s):  
Anthony Campbell ◽  
Yeqiao Wang
Keyword(s):  
Salt Marsh ◽  
Satellite Imagery ◽  
Salt Marshes ◽  
Wave Attenuation ◽  
Change Analysis ◽  
Object Based ◽  
Island System ◽  
High Resolution Satellite Imagery ◽  
Assateague Island

Salt marshes provide extensive ecosystem services, including high biodiversity, denitrification, and wave attenuation. In the mid-Atlantic, sea level rise is predicted to affect salt marsh ecosystems severely. This study mapped the entirety of Assateague Island with Very High Resolution satellite imagery and object-based methods to determine an accurate salt marsh baseline for change analysis. Topobathy-metric light detection and ranging was used to map the salt marsh and model expected tidal effects. The satellite imagery, collected in 2016 and classified at two hierarchical thematic schemes, were compared to determine appropriate thematic richness. Change analysis between this 2016 map and both a manually delineated 1962 salt marsh extent and image classification of the island from 1994 determined rates off change. The study found that from 1962 to 1994, salt marsh expanded by 4.01 ha/year, and from 1994 to 2016 salt marsh was lost at a rate of -3.4 ha/ year. The study found that salt marsh composition, (percent vegetated salt marsh) was significantly influenced by elevation, the length of mosquito ditches, and starting salt marsh composition. The study illustrates the importance of remote sensing monitoring for understanding site-specific changes to salt marsh environments and the barrier island system.

scholarly journals Classification of Soil Desalination Areas Using High Resolution Satellite Imagery in Saemangeum Reclaimed Land

2013 ◽  
Vol 46 (6) ◽  
pp. 426-433 ◽  
Author(s):  
Kyung-Do Lee ◽  
Shin-Chul Baek ◽  
Suk-Young Hong ◽  
Yi-Hyun Kim ◽  
Sang-Il Na ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Reclaimed Land ◽  
Soil Desalination ◽  
High Resolution Satellite Imagery

scholarly journals Identifikasi Kerusakan Pasca Gempa Menggunakan Metode Object Based Image Analysist (OBIA) (Studi Kasus: Pidie Jaya, Aceh)

2017 ◽  
Author(s):  
Prila Ayu Dwi Prastiwi ◽  
agung budi harto ◽  
Ketut Wikantika ◽  
Dasapta Erwin Irawan
Keyword(s):  
Satellite Imagery ◽  
Damage Identification ◽  
Earthquake Damage ◽  
Tectonic Plates ◽  
Sensing Technology ◽  
Australian Plate ◽  
Object Based ◽  
Richter Scale ◽  
The Pacific ◽  
High Resolution Satellite Imagery

AbstrakIndonesia merupakan salah satu negara yang dilalui oleh pertemuan tiga lempeng aktif, yai- tuLempeng Indo-Australia, Lempeng Euro-Asia, danLempengPasifik. Kondisiitumenjadis- alahsatupenyebab seringterjadinyabencanaalam, khususnyagempabumi. Padatanggal 7 Desember 2016 Kota Aceh kembali diguncang gempa bumi dengan kekuatan 6,5 skala richter. Gempa bumi tersebut mengakibat ratusan bangunan mengalami kerusakan. Saat ini teknologi penginderaan jauh sangat berperan dalam melakukan identifikasi kerusakan akibat gempa. Penelitian kali ini akan berfokus pada identifikasi kerusakan bangunan yang diakibatkan oleh gempa bumi dengan menggunakan citra satelit beresolusi tinggi, yaitu citra Pleiades yang diambil tanggal 7 Desember 2016. Metode yang digunakan untuk identifikasi kerusakan bangunan adalah metode Object Based Image Analysist (OBIA). Pada proses klasifikasi, metode OBIA memandang objek tidak hanya berdasarkan nilai piksel saja melainkan berdasarkan bentuk, luasan, dan tekstur disekitarnya. Berdasarkan penelitian ini didapatkan bahwa metode OBIA terbukti dapat mengidentifikasi kerusakan bangunan pasca gempa bumi secara cepat.Kata kunci: OBIA, Citra satelit, Klasifikasi, Kerusakan pasca gempa bumi Abstract277 Jalan Ganesha No.10, Kota Bandung, Jawa Barat 40132 Indonesia e-mail: [email protected] is located in a meeting point of three active tectonic plates, the Indo-Australian Plate, Euro-Asia Plate, and the Pacific Plate. This condition causes frequent occurrence of natural disasters, especially earthquakes. On December 7, 2016 Aceh was hit by an earth- quake with a magnitude of 6.5 richter scale. The earthquake affected damaged hundreds of buldings. Nowadays remote sensing technology can be used to identify damage caused by the earthquake. This research is focused on post-earthquake damage identification using high resolution satellite imagery, the Pleiades image taken on December 7, 2016. The method used to identify the damaged buildings is the Object Based Image Analysist (OBIA) method. In the classification process, the OBIA method distinguish objects not only based on pixel values but also on the basis of the shape, area, and texture around them. This re-search has proven that OBIA method quickly identifies the damage buildings caused by the earthquake.Keywords: OBIA, Satellite Imagery, Classification, Post-Earthquake Damage

scholarly journals Using Multispectral Drone Imagery for Spatially Explicit Modeling of Wave Attenuation through a Salt Marsh Meadow

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 25
Author(s):  
Antoine Mury ◽  
Antoine Collin ◽  
Thomas Houet ◽  
Emilien Alvarez-Vanhard ◽  
Dorothée James
Keyword(s):  
Salt Marsh ◽  
Salt Marshes ◽  
Crop Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Wave Attenuation ◽  
Coefficient Of Determination ◽  
Coastal Protection ◽  
Surface Model ◽  
Red Edge

Offering remarkable biodiversity, coastal salt marshes also provide a wide variety of ecosystem services: cultural services (leisure, tourist amenities), supply services (crop production, pastoralism) and regulation services including carbon sequestration and natural protection against coastal erosion and inundation. The consideration of this coastal protection ecosystem service takes part in a renewed vision of coastal risk management and especially marine flooding, with an emerging focus on “nature-based solutions.” Through this work, using remote-sensing methods, we propose a novel drone-based spatial modeling methodology of the salt marsh hydrodynamic attenuation at very high spatial resolution (VHSR). This indirect modeling is based on in situ measurements of significant wave heights (Hm0) that constitute the ground truth, as well as spectral and topographical predictors from VHSR multispectral drone imagery. By using simple and multiple linear regressions, we identify the contribution of predictors, taken individually, and jointly. The best individual drone-based predictor is the green waveband. Dealing with the addition of individual predictors to the red-green-blue (RGB) model, the highest gain is observed with the red edge waveband, followed by the near-infrared, then the digital surface model. The best full combination is the RGB enhanced by the red edge and the normalized difference vegetation index (coefficient of determination (R2): 0.85, root mean square error (RMSE): 0.20%/m).

Mapping changes in the largest continuous Amazonian mangrove belt using object-based classification of multisensor satellite imagery

2013 ◽  
Vol 117 ◽  
pp. 83-93 ◽  
Author(s):  
Wilson R. Nascimento ◽  
Pedro Walfir M. Souza-Filho ◽  
Christophe Proisy ◽  
Richard M. Lucas ◽  
Ake Rosenqvist
Keyword(s):  
Satellite Imagery ◽  
Object Based
Sign in / Sign up

Export Citation Format

Share Document